Per-User Retrieval

When building a retrieval app, you often have to build it with multiple users in mind. This means that you may be storing data not just for one user, but for many different users, and they should not be able to see eachother’s data. This means that you need to be able to configure your retrieval chain to only retrieve certain information. This generally involves two steps.

Step 1: Make sure the retriever you are using supports multiple users

At the moment, there is no unified flag or filter for this in LangChain. Rather, each vectorstore and retriever may have their own, and may be called different things (namespaces, multi-tenancy, etc). For vectorstores, this is generally exposed as a keyword argument that is passed in during similarity_search. By reading the documentation or source code, figure out whether the retriever you are using supports multiple users, and, if so, how to use it.

Note: adding documentation and/or support for multiple users for retrievers that do not support it (or document it) is a GREAT way to contribute to LangChain

Step 2: Add that parameter as a configurable field for the chain

This will let you easily call the chain and configure any relevant flags at runtime. See this documentation for more information on configuration.

Step 3: Call the chain with that configurable field

Now, at runtime you can call this chain with configurable field.

Code Example

Let’s see a concrete example of what this looks like in code. We will use Pinecone for this example.

import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone

/Users/harrisonchase/.pyenv/versions/3.10.1/envs/langchain/lib/python3.10/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
  from tqdm.autonotebook import tqdm

# The environment should be the one specified next to the API key
# in your Pinecone console
pinecone.init(api_key="...", environment="...")
index = pinecone.Index("test-example")
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone(index, embeddings, "text")

vectorstore.add_texts(["i worked at kensho"], namespace="harrison")
vectorstore.add_texts(["i worked at facebook"], namespace="ankush")

['ce15571e-4e2f-44c9-98df-7e83f6f63095']

The pinecone kwarg for namespace can be used to separate documents

# This will only get documents for Ankush
vectorstore.as_retriever(search_kwargs={"namespace": "ankush"}).get_relevant_documents(
    "where did i work?"
)

[Document(page_content='i worked at facebook')]

# This will only get documents for Harrison
vectorstore.as_retriever(
    search_kwargs={"namespace": "harrison"}
).get_relevant_documents("where did i work?")

[Document(page_content='i worked at kensho')]

We can now create the chain that we will use to do question-answering over

from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import (
    ConfigurableField,
    RunnableBinding,
    RunnableLambda,
    RunnablePassthrough,
)

This is basic question-answering chain set up.

template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)

model = ChatOpenAI()

retriever = vectorstore.as_retriever()

Here we mark the retriever as having a configurable field. All vectorstore retrievers have search_kwargs as a field. This is just a dictionary, with vectorstore specific fields

configurable_retriever = retriever.configurable_fields(
    search_kwargs=ConfigurableField(
        id="search_kwargs",
        name="Search Kwargs",
        description="The search kwargs to use",
    )
)

We can now create the chain using our configurable retriever

chain = (
    {"context": configurable_retriever, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)

We can now invoke the chain with configurable options. search_kwargs is the id of the configurable field. The value is the search kwargs to use for Pinecone

chain.invoke(
    "where did the user work?",
    config={"configurable": {"search_kwargs": {"namespace": "harrison"}}},
)

'The user worked at Kensho.'

chain.invoke(
    "where did the user work?",
    config={"configurable": {"search_kwargs": {"namespace": "ankush"}}},
)

'The user worked at Facebook.'

Code Example​

Code Example